code: def __init__(self, dim_in):
      super(Preprocess, self).__init__()
      self.dim_dict = {
          name: dim
          for name, dim in zip(
              cfg.dataset.augment_feature,
              cfg.dataset.augment_feature_dims,
          )
      }
      self.dim_dict["node_feature"] = dim_in
      self.dim_out = sum(self.dim_dict.values())
  
imports: import logging
  import networkx
  import numpy
  import torch
  import torch.nn
  from deepsnap.graph import Graph
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.contrib.transform.identity import compute_identity
label_desc: Use image node_id 1 to create a new Preprocess object from inherited base classes: nn with example: obj = Preprocess(dim_in)